The present invention relates to navigation systems, and in particular the invention relates to personal navigation systems.
Reliable navigation systems have always been essential for estimating both distance traveled and position. Some of the earliest type of navigation systems relied upon navigation by stars, or celestial navigation. Prior to the development of celestial navigation, navigation was done by xe2x80x9cdeducedxe2x80x9d (or xe2x80x9cdeadxe2x80x9d) reckoning. In dead-reckoning, the navigator finds his position by measuring the course and distance he has moved from some known point. Starting from a known point the navigator measures out his course and distance from that point. Each ending position would be the starting point for the course-and-distance measurement.
In order for this method to work, the navigator needs a way to measure his course, and a way to measure the distance moved. Course is measured by a magnetic compass. Distance is determined by a time and speed calculation: the navigator multiplied the speed of travel by the time traveled to get the distance. This navigation system, however, is highly prone to errors, which when compounded can lead to highly inaccurate position and distance estimates.
An example of a more advanced navigation system is an inertial navigation system (INS). The basic INS consists of gyroscopes, accelerometers, a navigation computer, and a clock. Gyroscopes are instruments that sense angular rate. They are used to give the orientation of an object (for example: angles of roll, pitch, and yaw of an airplane). Accelerometers sense a linear change in rate (acceleration) along a given axis.
In a typical INS, there are three mutually orthogonal gyroscopes and three mutually orthogonal accelerometers. This accelerometer configuration will give three orthogonal acceleration components which can be vectorially summed. Combining the gyroscope-sensed orientation information with the summed accelerometer outputs yields the INS""s total acceleration in 3D space. At each time-step of the system""s clock, the navigation computer time integrates this quantity once to get the body""s velocity vector. The velocity vector is then time integrated, yielding the position vector. These steps are continuously iterated throughout the navigation process.
Global Positioning System (GPS) is one of the most recent developments in navigation technology. GPS provides highly accurate estimates of position and distance traveled. GPS uses satellites to transmit signals to receivers on the ground. Each GPS satellite transmits data that indicates its location and the current time. All GPS satellites synchronize operations so that these repeating signals are transmitted at the same instant. The signals, moving at the speed of light, arrive at a GPS receiver at slightly different times because some satellites are farther away than others. The distance to the GPS satellites can be determined by estimating the amount of time it takes for their signals to reach the receiver. When the receiver estimates the distance to at least four GPS satellites, it can calculate its position in three dimensions.
When available, positioning aids such as GPS control navigation error growth. GPS receivers, however, require an unobstructed view of the sky, so they are used only outdoors and they often do not perform well within forested areas or near tall buildings. In these situations, an individual using a GPS is without an estimate of both distance traveled and position. Therefore, a need exists for a system that integrates the best navigation features of known navigation techniques to provide an individual with estimates of position and distance traveled, regardless of where they might travel.
The present invention provides solutions to the above-identified problems. In an exemplary embodiment, the present invention integrates traditional inertial navigation and independent measurements of distance traveled to achieve optimal geolocation performance in the absence of GPS or other radio-frequency positioning aids. The present invention also integrates the use of GPS to control navigation error growth. However, when GPS signals are jammed or unavailable, the present system still provides a useful level of navigation performance.
The expected performance characteristics of reasonably priced INS sensors, in particular the gyroscopes, have little practical value for long-term navigation applications ( greater than 60 seconds) using inertial navigation algorithms alone. Dead reckoning techniques provide a better long-term solution; however, for best performance, these techniques require motion that is predictable (i.e., nearly constant step size and in a fixed direction relative to body orientation). Unusual motions (relative to walking) such as sidestepping are not handled and can cause significant errors if the unusual motion is used for an extended period of time. Integrating traditional inertial navigation and independent measurements of distance traveled offers a solution to achieve optimal geolocation performance in the absence of GPS or other radio-frequency positioning aids.
In one exemplary embodiment, the invention provides a navigation system for mounting on a human. The navigation system includes one or more motion sensors for sensing motion of the human and outputting one or more corresponding motion signals. An inertial processing unit coupled to one or more of motion sensors determines a first position estimate based on one or more of the corresponding signals from the motion sensors. A distance traveled is determined by a motion classifier coupled to one or more of the motion sensors, where the distance estimate is based on one or more of the corresponding motion signals. In one embodiment, the motion classifier includes a step-distance model and uses the step-distance model with the motion signals to determine the distance estimate.
A Kalman filter is also integrated into the system, where the Kalman filter receives the first position estimate and the distance estimate and provides corrective feedback signals to the inertial processor for the first position estimate. In one embodiment, the Kalman filter determines the corrective feedback signals based on the first position estimate and the distance estimate and past and present values of the motion signals. In an additional embodiment, input from a position indicator, such as a GPS, provides a third position estimate, and where the Kalman filter provides corrections to the first position estimate and the distance estimate using the third position estimate. The Kalman filter also provides corrections (e.g., modifications) to parameters of the motion model based on the errors in the distance estimate. In one embodiment, the modifications to the model parameters are specific to one or more humans.
The present invention also provides for a motion classification system. The motion classification system includes first sensors coupled to a processor to provide a first type of motion information, and second sensors coupled to the processor to provide a second type of motion information. In one exemplary embodiment, the first sensors are a triad of inertial gyroscopes and the second sensors are a triad of accelerometers. A neural-network is then employed to analyze the first and second types of motion information to identify a type of human motion. The neural-network is used to identify the type of human motion as either walking forward, walking backwards, running, walking down or up an incline, walking up or down stairs, walking sideways, crawling, turning left, turning right, stationary, or unclassifiable. Once identified, motion models specific for the motion type are used to estimate a distance traveled. The distance traveled estimate is then used with the navigation system for mounting on the human to provide distance traveled and location information as described above.